An object-oriented modelling framework for the arterial wall

An object-oriented modelling framework for the arterial wall is presented. The novelty of the framework is the possibility to generate customisable artery models, taking advantage of imaging technology. In our knowledge, this is the first object-oriented modelling framework for the arterial wall. Existing models do not allow close structural mapping with arterial microstructure as in the object-oriented framework. In the implemented model, passive behaviour of the arterial wall was considered and the tunica adventitia was the objective system. As verification, a model of an arterial segment was generated. In order to simulate its deformation, a matrix structural mechanics simulator was implemented. Two simulations were conducted, one for an axial loading test and other for a pressure–volume test. Each simulation began with a sensitivity analysis in order to determinate the best parameter combination and to compare the results with analogue controls. In both cases, the simulated results closely reproduced qualitatively and quantitatively the analogue control plots.     

Variation in the significant environmental factors affecting larval abundance of four major Chinese carp species: fish spawning response to the Three Gorges Dam

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Paternal care in a fish: epigenetics and fitness enhancing effects on offspring anxiety

In many animals, including humans, interactions with caring parents can have long-lasting effects on offspring sensitivity to stressors. However, whether these parental effects impact offspring fitness in nature is often unclear. In addition, despite evidence that maternal care can influence offspring behaviour via epigenetic alterations to the genome, it remains unclear whether paternal care has similar effects. Here, we show in three-spined sticklebacks, a fish in which fathers are the sole provider of offspring care, that the direct care provided by fathers affects offspring anxiety and the potential for epigenetic alterations to the offspring genome. We find that families are differentially vulnerable to early stress and fathers can compensate for this differential sensitivity with the quality of their care. This variation in paternal care is also linked to the expression in offspring brains of a DNA methyltransferase (Dnmt3a) responsible for de novo methylation. We show that these paternal effects are potentially adaptive and anxious offspring are unlikely to survive an encounter with a predator. By supplying offspring care, fathers reduce offspring anxiety thereby increasing the survival of their offspring—not in the traditional sense through resource provisioning but through an epigenetic effect on offspring behavioural development.     

Rule extraction in gene–disease relationship discovery

Background

Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.

Methods

Our study uses the automatic rule-learning approach to gene–disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.

Results

We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.

Conclusions

We employ the rule-learning approach to extract gene–disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.     

Novel quantitative tools for engineering analysis of hepatocyte cultures in bioartificial liver systems

Extracorporeal bioartificial liver devices (BAL) are perhaps among the most promising technologies for the treatment of liver failure, but significant technical challenges remain in order to develop systems with sufficient processing capacity and of manageable size. One key limitation is that during BAL operation, when the device is exposed to plasma from the patient, hepatocytes are prone to accumulate intracellular lipids and exhibit poor liver-specific functions. Based on hepatic intermediary metabolism, we have utilized mathematical programming techniques to optimize the biochemical environment of hepatocyte cultures towards the desired effect of increased albumin and urea synthesis. To investigate the feasible range of optimal hepatic function, we have obtained a Pareto optimal set of solutions corresponding to liver-specific functions of urea and albumin secretion in the metabolic framework using multiobjective optimization. The importance of amino acids in the supplementation and the criticality of the metabolic pathways have been investigated using logic-based programming techniques. Since the metabolite measurements are bound to be patient specific, and hence subject to variability, uncertainty has to be integrated with system analysis to improve the prediction of hepatic function. We have used the concept of two stage stochastic programming to obtain robust solutions by considering extracellular variability. The proposed analysis represents a new systematic approach to analyze behavior of hepatocyte cultures and optimize different operating parameters for an extracorporeal device based on real-time conditions.     

基于多尺度标记优化分水岭方法的油茶冠幅提取

针对尺度对地物空间结构的限制以及传统分水岭分割易产生树冠过分割等问题,选择长沙县明月村油茶基地为研究区,提出一种基于多尺度标记优化分水岭分割油茶树冠的方法.首先使用高分辨率无人机影像采集图像,分析影像特征,构建油茶分类体系,提取油茶林分布区域.其次,运用多尺度区域迭代增长方法提取树冠标记,将标记应用于多阈值尺度的分水岭...     

Perinatal high fat diet and early life methyl donor supplementation alter one carbon metabolism and DNA methylation in the brain

One carbon metabolism is regulated by the availability of nutrients known as methyl donors, and disruption of this pathway can affect multiple physiological systems. DNA methylation, critical for the regulation of gene expression, is linked to one carbon metabolism, and can be altered by perinatal diet. In this study, dams (n  = 12/group) were fed HF or standard control (SC ) diet through pregnancy and lactation, and male and female offspring were then fed either SC or methyl donor‐supplemented diet (MDS ) between 3 and 6 weeks of age (n  = 20–26/group). Concentration of one carbon intermediates and other related metabolites were assessed within brain tissue (prefrontal cortex, PFC ) through the use of mass spectrometry at 6 weeks of age. In addition, the expression of target genes and enzymes that participate in DNA methylation or are relevant to one carbon metabolism were measured. We found that MDS increases the concentration of folate intermediates in the PFC , and that this increase is blunted in male offspring from dams fed a HF diet. In addition, perinatal HF diet increased the concentration of cysteine in the PFC of both male and female offspring, consistent with oxidative stress. Furthermore, both maternal HF diet and postnatal MDS altered global DNA methylation in the PFC in males but not females. Collectively, these data demonstrate sex differences in changes in one carbon metabolites in the prefrontal cortex in response to early life high fat diet and methyl donor supplementation.

Read the Editorial Highlight for this article on page 358

     

A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network

Recently, governmental legislations, limitation of natural resources and adverse effects of End-of-Life products on ecological system have spurred researchers to design closed-loop supply chains (CLSCs). Accordingly, designing green supply chains (SCs) that manage greenhouse gas emissions and prevent air pollution can be helpful for companies to heighten profitability and customer loyalty, besides, uncertainty of parameters and disruption strikes could adversely affect performance of SCs and lower quality of output decisions. Effective planning prevents great losses and increases reliability of manager's decisions against uncertainties. Therefore, this paper is proceeding to design a reliable bi-objective green CLSC that minimizes total costs of network aside with minimizing harmful gas emissions. The proposed model is capable of controlling adverse effects of disruptions via applying scenario-based stochastic programming approach. Also, an effective hybrid robust fuzzy stochastic programming method is extended to effectively control uncertainty of parameters and risk-aversion level of output decisions. Extended model analyzing is based on lead-acid battery SC case study that output results approve applicability and effectiveness of model.     

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases

Offspring exposed to gestational diabetes mellitus (GDM) have an increased risk for chronic diseases, and one promising mechanism for fetal metabolic programming is epigenetics. Therefore, we postulated that GDM exposure impacts the offspring’s methylome and used an epigenomic approach to explore this hypothesis. Placenta and cord blood samples were obtained from 44 newborns, including 30 exposed to GDM. Women were recruited at first trimester of pregnancy and followed until delivery. GDM was assessed after a 75-g oral glucose tolerance test at 24–28 weeks of pregnancy. DNA methylation was measured at > 485,000 CpG sites (Infinium HumanMethylation450 BeadChips). Ingenuity Pathway Analysis was conducted to identify metabolic pathways epigenetically affected by GDM. Our results showed that 3,271 and 3,758 genes in placenta and cord blood, respectively, were potentially differentially methylated between samples exposed or not to GDM (p-values down to 1 × 10⁻⁰⁶; none reached the genome-wide significance levels), with more than 25% (n = 1,029) being common to both tissues. Mean DNA methylation differences between groups were 5.7 ± 3.2% and 3.4 ± 1.9% for placenta and cord blood, respectively. These genes were likely involved in the metabolic diseases pathway (up to 115 genes (11%), p-values for pathways = 1.9 × 10⁻¹³ < p < 4.0 × 10⁻⁰³; including diabetes mellitus p = 4.3 × 10⁻¹¹). Among the differentially methylated genes, 326 in placenta and 117 in cord blood were also associated with newborn weight. Our results therefore suggest that GDM has epigenetic effects on genes preferentially involved in the metabolic diseases pathway, with consequences on fetal growth and development, and provide supportive evidence that DNA methylation is involved in fetal metabolic programming.     

基于可见光植被指数的面向对象湿地水生植被提取方法

利用ESP分割工具确定最佳分割尺度,通过多尺度分割算法创建最优分割影像,基于微型无人机影像数据生成可见光植被指数,从一系列可见光植被指数中选取一组最优植被指数,建立决策树规则,利用隶属度函数对研究区自动分类,生成水生植被分布图.结果表明: 监督分类法的总体精度为53.7%,面向对象分类法总体精度为91.7%,与基于像元的监督分类法相比,面向对象分类法显著改善了影像分类结果,并大大提高了水生植被提取精度,监督分类法的Kappa系数为0.4,而面向对象分类法的Kappa系数为0.9.这表明利用微型无人机数据生成的可见光植被指数结合面向对象分类方法提取水生植被在该研究区是可行的,并能够应用到其他类似区域.